Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34357
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Lavinas, Yuri
Aranha, Claus
Ochoa, Gabriela
Contact Email: goc@cs.stir.ac.uk
Title: Search Trajectories Networks of Multiobjective Evolutionary Algorithms
Editor(s): Jiménez Laredo, Juan Luis
Hidalgo, J. Ignacio
Babaagba, Kehinde Oluwatoyin
Citation: Lavinas Y, Aranha C & Ochoa G (2022) Search Trajectories Networks of Multiobjective Evolutionary Algorithms. In: Jiménez Laredo JL, Hidalgo JI & Babaagba KO (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, 13224. EvoApplications 2022, Madrid, Spain, 20.04.2022-22.04.2022. Cham, Switzerland: Springer International Publishing, pp. 223-238. https://doi.org/10.1007/978-3-031-02462-7_15
Issue Date: 2022
Date Deposited: 24-May-2022
Series/Report no.: Lecture Notes in Computer Science, 13224
Conference Name: EvoApplications 2022
Conference Dates: 2022-04-20 - 2022-04-22
Conference Location: Madrid, Spain
Abstract: Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our approach uses the idea of decomposition, where a multiobjective problem is transformed into several single-objective problems. We show that STNs can be used to model and distinguish the search behavior of two popular multiobjective algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2 and 3 objectives. Our findings suggest that we can improve our understanding of MOEAs using STNs for algorithm analysis.
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of a paper published in Jiménez Laredo JL, Hidalgo JI & Babaagba KO (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, 13224. EvoApplications 2022, Madrid, Spain, 20.04.2022-22.04.2022. Cham, Switzerland: Springer International Publishing, pp. 223-238. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-02462-7_15
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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